6 Backup Strategy Record Artefacts We can t stop someone s heart bea>ng can we? Some artefacts can t be avoided (e.g. heart beat) Recording these artefacts gives us a bezer chance to detect and remove them Record ECG, Eyetracker, EOG, ( EMG, Respira>on) This may be restricted by your specific experimental constraints. THE MORE EXTERNAL SIGNALS THE BETTER!

8 Visual Inspec>on is EssenAal! All clever artefact rejecaons tools fail at some point If running the manual pipeline, you must check the output at each stage. e.g. use oslview to check pre- epoched data. You will play with this today If running the automated pipeline (OPT) inspect the diagnos*c output plots See later

10 Maxfilter Maxfilter is a program provided by Elekta, which implements a spatial signal space separation (SSS) algorithm to remove the external noise (bout):

11 Movement Compensa>on Maxfilter can use MaxMove to compensate for head movements by reprojecting the data onto the sensors as if it had been recorded with the head in a different position. This can be used in two ways: 1) to continuously compensate for movements made within a recording session (-movecomp option) - requires that the HPI signal from the coils was recorded continuously during the MEG session 2) to bring different sessions / subjects into a common frame, making the sensor-space results more comparable between sessions / subjects (-trans option)

12 Maxfilter Maxfilter can also: detect bad channels downsample data, output log files for head posi*on, and other things besides - see the manual for the full set of op*ons There is a func>on to call MaxFilter called osl_call_maxfilter.m

13 Double Maxfilter Procedure We advise you use the following Double Maxfilter Procedure when using MaxFilter. 1. Call osl_call_maxfilter without MaxFilter S.nosss = 1; 2. Convert to SPM and open in oslview 3. Mark any channels with scanner artefacts as Bad. 4. Call osl_call_maxfilter with MaxFilter & bad channels. S.nosss = 0; S.spmfile points to the SPM file from steps 2 & 3.

16 The Central Limit Theorem Non- Gaussianity (kurtosis) A mixture of signals is always more Gaussian than the underlying signals. As long as there are enough signals! White noise sources Mixture of sources Number of mixed signals By searching for the set of maximally non- Gaussian signals we can reverse the mixing process and recover our unknown sources. That s ICA!

17 Classifying Components ICA un- mixes our MEG data but doesn t tell us which components are artefacts AfRICA has two ways of helping you do this: 1.) Correla>on with external signals If you have acquired ECG, Eyetracker, EOG etc AfRICA will flag components that match these. 2.) Extreme temporal kurtosis ( peakedness of the distribu>on ) Extreme high and low kurtosis. You can see both at work in osl_example_africa.m

18 Classifying Components ICA un- mixes our MEG data but doesn t tell us which components are artefacts AfRICA can be run in two modes: 1.) Manual In which you manually label components as artefacts (AFRICA will offer up those that are artefact channel correlated or have extreme kurtosis) 2.) Automated AFRICA automa>cally thresholds artefact channel correla>ons and kurtosis (used by OPT)

22 OPT - Data Input Data can be input as: Either (only for Elekta Neuromag data): Or: Or: - the full path of the raw fif files (pre-sss) to pass to the Maxfilter - the full path of the input files that will be passed to the SPM convert function (for Elekta Neuromag data this will be post-sss.fif files - the full path of the (already converted) SPM MEEG files

23 Using OPT Use osl_check_opt call to setup an OPT struct: opt= osl_check_opt(opt); Requires limited mandatory settings Fills other field with default values (which can then be adjusted before running) Use osl_run_opt to run an OPT: opt=osl_run_opt(opt);